{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'google', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "\n",
    "def sort_data(rets):\n",
    "    ins = []\n",
    "    outs = []\n",
    "    for i in range(len(rets)-100):\n",
    "        ins.append(rets[i:i+100].tolist())\n",
    "        outs.append(rets[i+100])\n",
    "    return np.array(ins), np.array(outs)\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol_list = ['C', 'GS']\n",
    "rets = get_data(symbol_list)\n",
    "ins, outs = sort_data(rets)\n",
    "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n",
    "div = int(.8 * ins.shape[0])\n",
    "train_ins, train_outs = ins[:div], outs[:div]\n",
    "test_ins, test_outs = ins[div:], outs[div:]\n",
    "\n",
    "#normalize inputs\n",
    "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "positions = tf.constant([-1,0,1]) #long, neutral or short\n",
    "num_positions = 3\n",
    "num_symbols = len(symbol_list)\n",
    "num_samples = 20\n",
    "\n",
    "n_input = num_symbols * 100\n",
    "n_hidden_1 = 10 # 1st layer number of features\n",
    "n_hidden_2 = 10 # 2nd layer number of features\n",
    "n_classes = num_positions * num_symbols # MNIST total classes (0-9 digits)\n",
    "\n",
    "\n",
    "# define placeholders \n",
    "x = tf.placeholder(tf.float32, [None, num_symbols * 100])\n",
    "y_ = tf.placeholder(tf.float32, [None,  num_symbols])\n",
    "\n",
    "weights = {\n",
    "    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "    'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "}\n",
    "\n",
    "def multilayer_perceptron(x, weights, biases):\n",
    "    # Hidden layer with RELU activation\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "    layer_1 = tf.nn.relu(layer_1)\n",
    "    # Hidden layer with RELU activation\n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "    layer_2 = tf.nn.relu(layer_2)\n",
    "    # Output layer with linear activation\n",
    "    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "    return out_layer\n",
    "\n",
    "# Construct model\n",
    "y = multilayer_perceptron(x, weights, biases)\n",
    "\n",
    "\n",
    "\n",
    "# loop through symbol, taking the columns for each symbol's bucket together\n",
    "pos = {}\n",
    "sample_n = {}\n",
    "sample_mask = {}\n",
    "symbol_returns = {}\n",
    "relevant_target_column = {}\n",
    "for i in range(num_symbols):\n",
    "    # isolate the buckets relevant to the symbol and get a softmax as well\n",
    "    symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n",
    "    symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n",
    "    # sample probability to chose our policy's action\n",
    "    sample = tf.multinomial(tf.log(symbol_probs_softmax), num_samples)\n",
    "    for sample_iter in range(num_samples):\n",
    "        sample_n[i*num_samples + sample_iter] = sample[:,sample_iter]\n",
    "        pos[i*num_samples + sample_iter] = tf.reshape(sample_n[i*num_samples + sample_iter], [-1]) - 1\n",
    "        symbol_returns[i*num_samples + sample_iter] = tf.multiply(\n",
    "                                                            tf.cast(pos[i*num_samples + sample_iter], float32), \n",
    "                                                             y_[:,i])\n",
    "        \n",
    "        sample_mask[i*num_samples + sample_iter] = tf.cast(tf.reshape(tf.one_hot(sample_n[i*num_samples + sample_iter], 3), [-1,3]), float32)\n",
    "        relevant_target_column[i*num_samples + sample_iter] = tf.reduce_sum(\n",
    "                                                    symbol_probs_softmax * sample_mask[i*num_samples + sample_iter],1)\n",
    "    \n",
    "\n",
    "\n",
    "daily_returns_by_symbol_ = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in symbol_returns.values()])\n",
    "daily_returns_by_symbol = tf.transpose(tf.reshape(daily_returns_by_symbol_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "daily_returns = tf.reduce_mean(daily_returns_by_symbol, 2) # [?,5]\n",
    "\n",
    "total_return = tf.reduce_prod(daily_returns+1, 0)\n",
    "z = tf.ones_like(total_return) * -1\n",
    "total_return = tf.add(total_return, z)\n",
    "\n",
    "\n",
    "ann_vol = tf.multiply(\n",
    "    tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns, 0)),2),0)) ,\n",
    "    np.sqrt(252)\n",
    "    )\n",
    "sharpe = tf.div(total_return, ann_vol)\n",
    "#Maybe metric slicing later\n",
    "#segment_ids = tf.ones_like(daily_returns[:,0])\n",
    "#partial_prod = tf.segment_prod(daily_returns+1, segment_ids)\n",
    "\n",
    "\n",
    "training_target_cols = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n",
    "ones = tf.ones_like(training_target_cols)\n",
    "gradient_ = tf.nn.sigmoid_cross_entropy_with_logits(labels=training_target_cols, logits=ones)\n",
    "\n",
    "gradient = tf.transpose(tf.reshape(gradient_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "\n",
    "#cost = tf.multiply(gradient , daily_returns_by_symbol_reshaped)\n",
    "#cost = tf.multiply(gradient , tf.expand_dims(daily_returns, -1))\n",
    "#cost = tf.multiply(gradient , tf.expand_dims(total_return, -1))\n",
    "cost = tf.multiply(gradient , tf.expand_dims(sharpe, -1))\n",
    "\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)\n",
    "costfn = tf.reduce_mean(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0100 cost= -0.0511034 total return= -0.254313290 sharpe= -0.147050172\n",
      "Epoch: 0200 cost= 0.0008046 total return= 0.004304615 sharpe= 0.002454142\n",
      "Epoch: 0300 cost= -0.00654278 total return= -0.032786764 sharpe= -0.018854376\n",
      "Epoch: 0400 cost= -0.0104948 total return= -0.052206062 sharpe= -0.030145902\n",
      "Epoch: 0500 cost= 0.0085575 total return= 0.042194180 sharpe= 0.024507100\n",
      "Epoch: 0600 cost= -0.00712342 total return= -0.035571225 sharpe= -0.020566883\n",
      "Epoch: 0700 cost= 0.00835088 total return= 0.041818328 sharpe= 0.024149975\n",
      "Epoch: 0800 cost= 0.016556 total return= 0.083269991 sharpe= 0.048046894\n",
      "Epoch: 0900 cost= 0.0330436 total return= 0.164966017 sharpe= 0.095224828\n",
      "Epoch: 1000 cost= 0.0369702 total return= 0.184987023 sharpe= 0.106792770\n",
      "Epoch: 1100 cost= 0.0163765 total return= 0.081490621 sharpe= 0.047063101\n",
      "Epoch: 1200 cost= 0.0464217 total return= 0.231276035 sharpe= 0.133545727\n",
      "Epoch: 1300 cost= 0.0402187 total return= 0.201298326 sharpe= 0.116184413\n",
      "Epoch: 1400 cost= 0.0367863 total return= 0.183890969 sharpe= 0.106271721\n",
      "Epoch: 1500 cost= 0.0392539 total return= 0.196572214 sharpe= 0.113486424\n",
      "Epoch: 1600 cost= 0.0598056 total return= 0.299269736 sharpe= 0.172908783\n",
      "Epoch: 1700 cost= 0.0458882 total return= 0.230310053 sharpe= 0.133034244\n",
      "Epoch: 1800 cost= 0.0449383 total return= 0.225449950 sharpe= 0.130350322\n",
      "Epoch: 1900 cost= 0.0313403 total return= 0.157538086 sharpe= 0.090945952\n",
      "Epoch: 2000 cost= 0.0390447 total return= 0.195676491 sharpe= 0.112976611\n"
     ]
    }
   ],
   "source": [
    "# initialize variables to random values\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "# run optimizer on entire training data set many times\n",
    "train_size = train_ins.shape[0]\n",
    "for epoch in range(2000):\n",
    "    start = rng.randint(train_size-50)\n",
    "    batch_size = rng.randint(2,75)\n",
    "    end = min(train_size, start+batch_size)\n",
    "    \n",
    "    sess.run(optimizer, feed_dict={x: train_ins[start:end], y_: train_outs[start:end]})#.reshape(1,-1).T})\n",
    "    # every 1000 iterations record progress\n",
    "    if (epoch+1)%100== 0:\n",
    "        t,s, c = sess.run([ total_return, sharpe, costfn], feed_dict={x: train_ins, y_: train_outs})#.reshape(1,-1).T})\n",
    "        t = np.mean(t)\n",
    "        s = np.mean(s)\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\",c, \"total return=\", \"{:.9f}\".format(t), \n",
    "             \"sharpe=\", \"{:.9f}\".format(s))\n",
    "        #print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in sample results\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "d, t = sess.run([daily_returns, pos[0]], feed_dict={x: train_ins, y_: train_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/BqDs4EEUqxX/cePQ+fpqY1y+4x1/WIpXly7kL19O0T//kPKEmgv/w96TmRPj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\neKwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11202ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equity curve\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117bf6978>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cx4GPhoVPm6gts9ib6CUYmy4OV2rnCLcy1PMFqbwVGb181cdTbW0MZcJEjCfn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\nhof/D0ndEzxU2ID0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11202e400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#out of sample results\n",
    "d, t = sess.run([daily_returns, total_return], feed_dict={x: test_ins, y_: test_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2YBgGNf4atnq37uXMg4Aa49XMDN7PcPPE0nu/0imNPuHe8cS7IJCujTttSD4/\nnjoAw7CQ0MzFWfuKKfoHEYsnC0WL0qesBlkJYdEdBKMq/qjKVD2IYUhs3VrP708Uscjh4C4ighb+\nA/77Q6hdJN6fMxsKR8LHf8AwJN6L3cvqtS7410x+ekI58393EiNLslKnv7gsHftuGAbXPrucCx5b\nxJwNLTz++c4i8dPnVnDne5uo74rgjyZQYy5xbmgM7as+2Ol4AKIecO46qVxtZ5jTa5fif/9/ONZ3\nMda6KLWve6naSn0wOeF6jprRD4tm5YslR6a1H4v50WUbkrx/LhlZ0jGSi5Y6o52MfWYsZ75xJue8\ndQ66cYizsGoJumJDUDQbcxvmf6VTmpKGhf2TO+D+UT322Sxy0tI3J3L3FVP0DyJylhDf346/Dasl\ngUV3JC39BIOSZRaNYD4hb7p2rK9th1W5ugbz7oEt/4OVz/BG159Z8Wk7lIyDSCcN8dFs7xrMgsAV\nfFJ5Mkq0g765Lly2dOexps6ber28pos5G9KWVJNvZz9p97aOUJzFm2ohUYgmqSi6A2frzpa+rmrY\nYznY3Tu3pekG9Z0RykJJP72hcLK6c9rgauc4crVWThs3maglROWGwzOtxP4SjPrRZSuyZT9FXzYw\nJCH6CxsX9ti3pGnJfrW9vySiUQq23sLpm6+i2Yh/pTmH7klam2GAnoBHjoWkZW+1yGAopntnPzBF\n/yBiSYq+5vOjWFUsmoOl2zqI+uM4IsV0OpuRDStt28WDYaDz+XOL0w3UL4eY8HfqhkxjbDiL39wK\nOQMBqIxOQ7HKlPWDisgpxOs3AeDcQfQrW9Pxzm+sSme1HNPHQ6M3stND2Z3Sp9kXpXH1hyjRfrQW\nVqBKcVqjRTt9x6btlchY8GTvfGu1+KPENZ3iWpFy2toZxgrIZy0hWn4Ln2hH81/1JJSSkVjQyQh3\nEcxvJVp3ZN6mAZ8XJBlZ2b/MmLIsoctWMKDK27MS2Zq2NfvV9v4SjYjOv8w3jLgkEYjsuaSGYRg0\n+sQ9bu++F9sqoEsUkrEpMoZhMXPv7AdH5tN0mJISfb8Pq11H0Ry8uLSO/NZaJGQ29n0XgHB9H2KW\nCMv6/o/azQb1m5IPyvrXQLbCWQ8S1LNT7WoeIfpV+iC0oiBjThIhol3VNaDrXNd1L5Mk0QG0BWJ4\nw8kVv3Vepg/JZ90fZvK98X2IqXqPPCdAqhNo8UeRazdg1VyUjcnB524gEt9Z9LdXic8pKvLQ5Iuw\nfHv6Ia8QczvzAAAgAElEQVTpCGPTEjiahItJae3C0OG7mWXcEN7IQteJvJz/f2QWidDVzroK3Hk2\nLBHbTp9zJNDWLuoH2JT9yygqWyTReSTj9mVJZuUlK5GQDrkbJBTtOYfU3rnnnFD3zNlMJBnbbzMM\n+N4TyRO3AMl8VYa5Ind/MEX/INLt3tH9fmw2AxkZXyAMUeEmOevo8XS4RFRO1OpnwMhWfI5W3p+9\nmMizV4rJ2jHfhwmX0Xz+e6l26319MAwIqCWsS6zA2Vfkrumc9zpseJ3sVpWb5HfIcQkXwJaWIAlN\np7IlyMjSLDIdVkqzRa7/Rm9Pt0x3/p6ajjD+SAY6GsdNmYBkiYG+c+rbxkbhLsotLmPKn+dy/qOL\nSCQLnb+7tpFBoVYkXcdzztnIsTiBBgeRtu0AnHL0UO579y5KHhSpgv2Nm7E7Fay6He0ILJbe7hPh\ntIp9/4reyFYxUnAkRDtFriKsshWrbD3kFnEs1nMSvqNrz+nEl2/vpCRbjEztkgJDThU7GldB09qk\ne8eCZpiiv6+Yon8QsWRlgSyjdnVht4ufPocwUtKanjFmFi15YtIyI5bHL2b+jWUDXyGRcNCwrRMG\nTocTbwJ6+t/nvB2nXR2IXXPhdbayIbEGWdJZFLyUthfvYq7/l6zwX8bEAWJRz6raLhZVdxDX9NQk\nb6knKfo7FG33RxOE4kL0F1Q0EEv0w+tuZHTpCAxLAvlLoh+MqXj9Iv3C4vbM1PYtLQEC0QSvrKjn\nvBwhAnlXXYWRk42/xonWKfz6Nlc2ifp65O3b6Qi6UduqsTmFFRwIHb6LcULeGLUbv36abJ9fuDFk\n+/5Z+pIi7qXL808HIBAXc0KHQ46aeCIt+u6IQbt/z2HDgahKYaa4t7OKj8awJ0e0n98Lj03HcVcu\nk6UKjMNxIdq3BFP0DyKSxYIlNxetvQNnUsw8BFEMmYQcI9tTSt/RnbRkbGd96cfkuwu5bMpFAGw+\n+pdw0Sv49UKat/roaBcrW98a9RDRWJz39IcA8DlaeWD1/WT2tRPRs/nAK7I3GpLGoDw7A/Jc/Pl/\nFVz6b1G/d2yZeKj65bqQJNjYmF48VtuRzp9j7ajAEumPkeNDkiQki4as21F3sMBH3z6HxrY4CTnG\ny+uilCfTQXy4oYWF1R3EVZ1j9A4kmw3bwIFI/fqiRixYA8LdY7e6Um01NeZh9W7F4RSuHV8gPbl9\nuKBpOnOf2cRTNy3gnYfWEA19dYF9esPTNATEhLbi2D/3lcUm7qWTbWOYWjqVq8deLdqVlcPK0v/e\nQhvtwcY9HC1EP98Qq70/W/9rPvzXBjjmCigakzom1whj6OHdNWGyF0zRP8go+fmo7e04XcJKLrTH\nsBgKcUsEm9XJNTPv440x9+MZIyauRg8SBbK7vOLh+eCxdbx2zwqaP9PQJJVrT7uUGs8GQl3i4Z4x\n7gQag408NuAWXLYgfk3Exw+zbObGZdPony0E5icnDOKdn08TeXrUGJ5gNcf0zUrl9wGoaA4goXNX\n3v/4rfImVt1JQT8hzJKioWh2QlExB9C9wAzVSlwJ0RpQuXzaQLJdVh78pJJrn12B22Yhp7MZ24AB\nSIqCkpOPGpcpDm7CMMBWmY7kCUay6RNchyM5IvIF9p4xdN2mSh75yVw2V23/+n+YfWDVnFo2LUxH\nFtVXfLXax82hZu5bfh+ORLIWg9OxlzP2TPdIwb7pQx47ZTZXjL4C4LBw78QT6Y5wVEMZbeE9Z04N\nxlQ0WilIyIQCElUrWlnKLzF+Mh++cx+UHo0N0AxzIndfMUX/INMt+hkZQjwHZMlYdBuqRQxXc515\nLLxwIXec+SwAJVlFRJQAga4oIW+MtlohfkZcJmDv5JzBZ+MuStYNleOcfdTp3DjxRryJLmx56Vw2\nGyOn8Jr/V9w5vIZrTyjnt6cNY0yZBxpWwPu/gdnHcoflCTY0+vn7R2LSrKLJz/nWRZzYuZLW2HgA\nBo8cCoCsGCi6nVBAhICuq/fxN+tsnLoFVRGumGFFmTx28QT65Yrves7RfVDr67H17weAIy+PeFwh\nU/fRtjYT/e6/pK43Ec/CYURxhsUoIPgV3DuffLIcgM8WLP8af5F9p2pFS4/3tRu+movnva1iPsah\nir+b4sne0+F7RbYnO4+GNbD6v6nth4N7JxZPBwYEck7GG/Lu9ljDMAjGVML4GBDKT21f9u42Qt44\nTLoaZj2G1TDQjENXK+Lbjin6BxklPx+1o528DImEHGMEedglB7qS9lFm2jJxKML6c1ldRBwBYn6N\nitUixLLeI0Ie6/usR5EVckuEuKuWGAM8AxiWOwwAz3QLziwbNjmEjo3m8PFEN63g5jNGYJElCHcS\nf+w7LFjYwuzWZ2hsLWHSwFwe+qSSP7y9geU1XYxSVN7supPWwBno6IwZItpWrBISMt5OYbmtrW2j\nPG7HHRiLpoih99CiDI4tz+Ptnx/HoxdP4I6zRpKoq8PaV4i+PTcbEhKGAR2b0rmB7EOG4IkkaDRy\nsdbOAyAY2vtwXkuIuRGrbfdprg8U6z9voKOhZ0dUubzlK7l42oONZOg6v24T30nJ2rkA+NdBSbrA\nEpodwu0QaIaaRShqDFU9tPHskWRKb0ekBV/2YKTO3f8dw3ENTTfwSSFKYyICbfCEQgC6mpO/tcUm\nRB9T9PcVU/QPMkp+HmpjE5k/uQ8pvoLOrVnIqhMsu5+Y0pwxDK+VlR9vo9PZRMXRn7C8zwf0myqE\nsk+ZSLcQdvhQZIWByRDOtsJt/OjuY6h3pi3QuTXpWr7aqld4ou0pVnt/gqFnssI7mB9PGQDAUwu3\ns7rOS6eWHi3ErEGKssRDaE26Xbo6kwutmtez0Pdz0a4cpijLTrZLiFG2y8bpo4vR29sw4nFs/foC\nIppJ0g30uER6PS64Jk7E2trEU5xLQagCgFB47xN3RlL0LcreRX/jF4188vRGDP3rLYwK++Os/bSe\nef8VHW9xuZgI7zsyFzWus21N217b8LeuJ1PXhUgDzsz9c+8oTjEJr+o26KqBvw2D/5yONdiG2rFl\nv9r+2hgGtG5KvY1HhFtSwo+quLC3735NQjCmIil+6uQ4ufG+IMGUWYMA6GzaUfQxRX8/MEX/ILNj\nAfU+vm1Iug1nuA8ou7fIJE8CWzCDeLtEZfki7j3tLzinBLl2gijMPnRQP1aWfsSqMSLO32P3kOvI\n5e8r/s4jqx9BldNt+yOFkCxAsWTpFjDSEThyPIdJZc4en62r6clViy7y9QPYHMI14feJ4bqrdXP6\nHClKeX4GalsbkbXp4hnxWhG5Ye0rRN+StHDjIeGTVgoKsJaV4Rg5AlSVfsNPJcsQD3skLMSjozGI\nGt/1A9/tvk7Edu3v1VQNX6dob+HrVVQsamb95w00b/Pxyp+X0bzVx6fPVewxEueDx9cx/yUhpEMm\nFtFnWDK3fZ8MkCDQsffOyR/pJFOHLUWimlRmtmsvZ+wZW9JV2KH3h22fp7YrGCQOdrH0ZU/C7MlQ\nI9JrxKPib2VxJi1+387ZXjXdIJrQCEQT2IveAQOyvUeRV+omM090iF+8XMkTv/qcD19oxtl0IZK6\nf2GuvRlT9A8ykiMtsnly2j0gW+P433+f2LZt6OEw2847n/CyZWLnMW28PfJhXjjqTk448WgG5wxm\n9ozZ5DpECObw/OFYJndw++n/L9XeLcfeAsBTG55CtexQWDpagtEhcuwsbxZlHJsztrEtfxW2RDZ5\najt3z0pHShjxTLwO4cLxutOTcM7k5KM/KKKI/P70sN2mORjq1KicfjzbL/gBhipEOPDBHLBacQwf\nDohcRACv+qcDUPjb3zD444+w9Re1Ak7IddCWXIQWjcQJBiK8+KelvPHvdL6eHekO3d6xGPuOvPj6\nHJ77f0t49fVPcGWJUUjl8hbe/+c6WmsCrPqolo1fNPLOQ7tfxdpWk44iOvGiYWTmit9B3r4RV6aN\noHfvyeECaoQsScYfFJ1d2aDivZ6zJ6zJbJTLuZTtjUlXUc4ArAaoB3vCc/3r4v+QuFe6O2g5GXvv\nDPZ0ZTX5Isz4+zxOuPdT2oNxCq01nNxeRiSYx7hT+iJJEmXDRccaj6hUrvahdJ1KfrAfq1qPzJxM\n3zSm6B9ksmfNouTuu3FNmkReLIiBCHl06gkabvgNHY8/Qay6muiGDTTc8BsALh//Y3548lncctpv\nuWzUZTu1abfYeXzm4xxVeFRq28wBMxlbMBYAVU77ma26k7YtqyDQQiLah86CCs7+zViGlTuRkWmp\nreRHx/Zj0c0n88KV45ES2RhZbSwe8ypHX1SYasedtC4jyQnWYCxtrcYtCSatnpt6H6usRAuG8L72\nGp5zzkbJE6Od7hXK0cxkcfkMEdtv7SdE393WRLNtIAY6sYjKtnqRmndbxa4jQIyoEBY1uuuRQFON\nGJU0fBoj0CXEuanKR8QvOsWtq9KumVh4Z9/8+/9ci5pIh6ha1Biu2b9jRMWzZD55M+4MC6GuryD6\neoxMyYoa1LDGfdgLc/d6zp6w71B8pqFtMMhWghcvJDNaTEI/yG6QjuSK25joHLtF35oh3GhKPKfH\n4ffN2cK29hAt/hivrqjHYQngiZUBUJYsHHPa1aO58m/TGTktXYEuU3Px0MqHdnkJmzs3s6Fjw4H7\nTkcYpugfZGSnk+zvzcLi8eBM6IStIi4+tzMChkF00ya0TpG6QG0V4tYvqx+Xj76cMwaegUVO+6sN\nXcfQd79SdZBH+EO73Tt+hxC1irkvEX3pahyxIlyFGhOLJ5JXJIS4tk6EipZ4nEzOjWGL5+DI0Hj2\nZ7M5d9yZqbazMoVA+4PCwtd0hZDVx/yBr7DA4affwjkpiz3w0ceElyzGiMXwnHV2+rdIunfOKhIu\nI0uWaFMpLEByuYjX1pCTU0TCEiMeVWloEr+HruzaerVEkxOasV3/JmrSSFcSDtSYRukQMYrIyLUz\n5qSyHse+8MeeicoigTjb1qQLugw5ppD2fzxEdNVKSpoXIxs6dtW/k6Vv6AYvPPw5z9zzGS3bxd/a\nbyTIlG2gKljVQMpltq84s9IdblTL4N/Nj/H0LUsYv/F2XA3D96vtr4VhQCjZcYaFi0xNzrPYXTKy\nFgMp3cEtqGrntZX1XDVtIH1znby6op6obODUS5BkCXeyqo7DbcXhtnLij4bxg/8nDISyRDabOjft\nMovo+e+czw/f/eE3+U2/1Ziif4iQMzMhFKGyWESnFNUJKzZWVUW8IZ0ILbJh9xZLxchRNN18c+q9\noaroOyyGueGYG/j7iX8n1yr+zInsBjRJY7NvMnO3ilFB6WAhdiX9BgPQXLM9dX7ntvVYDGvKhdGN\n2tlJYb7oJBpbO1m6rRM0O5oSodXyA/wdI1G6Osi97MdYPB7aZ8+m/mc/R3a5cB2dHo10u3cSSQte\nTnYkkiRh69ePeE0NsiOLhCWC3iXT0iAsdUPZtfVqjYn5iN3VB5cDDrbmriFmFaOTQfleTjirkAtu\nGENeg3ClZXsryW9bTcgXJxpMW/tN1cKNVTokmyvvm87Mq0YT+PSznp/fXkewK4am6mxc0MiKD7bz\n4oNf0LleJbBV59W/LKeutpWAoVG2aSZxZx8s8v7XCnBmpS39JnkMET1tTfevytrVKd8MWoLfFuTx\nmdMJyUyq3TWDFIcNWY8gGel76Z45m+mf5+KGmcO4YIKY54lIOo54LhnZdrzPP0dw/hep4yVZQknO\nJeWqTkKJEDX+nllaD3kq6W8BpugfIiyZGeiBAMVH+3ln8K/pt3kJzqOPBlUl9MUCAGS3m5a7/7zL\ndLRGMv7Z99bbqW11113HlmMmpt577B5O7X8qFot48jwuCDrbiIcmsq3zRAL2DkaNFnH35QP6Epej\nNDfboXkdANWVInImtyztc45t3UbltOm4tooY9VFSA4+/+AqS7kSzRLn5OyMYFhEWuX3oUMoeeThl\n8WedczaSLb36VMnLA0kiWiE+x5KRnuRzDB9OZPUaJKsbf84ibA35BBcIUZeNnaNzorEY1mRaCBEN\n1BNN03FEM/A6WqnOF3WDIw/fg+WG79P2218hP34nJ2+8g6PWPERps5gz8Lam5ymat/qQFYmzfjkO\nu8tC4623kqjtmVJArl5PPKKyZm4dnz5bweI3t9JU5WdV2UcsHvIaAKs3bcIezEb3nyzOse1fCgaA\njB0mgn22AT32Kdr+jSK+DloizAcZbn5RXABhMVrtXiZgdziQ9QQgRLuqNciaOi+XTO6P02bhgol9\nyXTI3PlvnfxqJxm5dlru/jN1V19NvC5dA8KSTDmRmxCdx5eziNYHvlp1rt6MKfqHCNmdgR4K8ceZ\nT/C3/CuQEBOZSBLBuXOxeDwU3vg7IitW4Hv9jZ3OT7Sm/c96WIhT6PP5GIlEj4cEoFtWrDadSKFY\nNdowYwFjrncxpmwkAEWZhXjza4gGJhJ74VroqGZrvfCHDBsxNNVWZO0a0HVsCxcRUYJ0dX2P64P/\nwaI5MCxRTh1ZxIPHCuvSPngwrmOOYcArL1P2z9kU33Zbz9/A5cI+dChqs8g22Z2QDiDztJnoPh/2\neh+zbC9RnZuetLPEdk5b0O5Nr4bdVcBKXXMTFkNBylRZVfwJQybl4w6JEVVoYTIHfWszsqHjCosO\nLRUbDnQ0hMjOsRBbsZymm2/G96oQcfd0MQmdecbp5HRsBAwWvS6Sio29Nov/TLqJH7hf4t7gqxjo\n1NY2ceomcY4t7ie3eP8tU5fH3XODoTNt093IWhxpFx3kN0U8vsO6haR7R092OlanA4m06K+oEZ1C\nd/nPoiwHX9w4hX7tELfm4Hakf5fqU2eiJXPsd4t+ZkKhT0Yf3qx6s8c1VHbtOYuniSn6h4xuV4YR\nDlNsJEVyyBBh7QOWgnyyzzsP18SJNP3hD8TrG3qcr7amV4P633+/h2/f9/bbPY6VDPFntllkvnvh\nROpmfM7ts37Hd8u/28OfXDjBhaJl8HTtRfCP8bS1S0SUAMP7DE4dE9sswhW1BQswvlONoTlZHPkB\nFjUDrGL0EausRCkqwuIRPntLVhaZJ52EJO98u7kmjE//Ju60eLmPOw5LTg6uxeuZkAhw9Q9GM3/I\nSwRsXdhizp1GPx2+9EpPS2Lnz3nn1YXo6JQNziXg6CBjchOWHVIU9H3iCeTMTNwnHE/RzGlIukbn\nDmmhvS0hlA2Lqb3sstToqujmm3COEZFOtrIysiKNjMwTo5wJp/fH8AgXR1E0QJERJWTrwl8XJlud\nSkHbCv7t8lF46Vk7XevXxfqlNA72mJexc+cgGSoYB+8RjyeCYECxv1wsEtO1lOg7XC4wEkhJ0Q8n\nJ3g9znToZTDSTkfuSKLOfJxGz4Vv8a2iI7Ukaw9YdLhw+IWsaFnB35f/PXVcdz2BPEceJrvGFP1D\nhCVTuDL0QAA9EABJQna7yT7/fADU5hYki4XCG34NiQSxyp6LbLqtY0t2Nk1//BOBjz5O7Wt/+BFa\n77tvh6OTsfWyhcn9J/GX8/+A3bJzWuSLTz+Xmv7LSERG8EjzGxiBSUQy27Apacs6tnkzyDKa18tP\nC6ZRP2Yl8fA4nIlcpOQEa2z7dmzlA7/S7+CaNCl9lTt0CrLNRt7VV2Pdso1ol4I9qvLU9Q/iOjqK\nxbDStL1nnhufT4xKIlYfitpzJKCqKvrmTILl9UwZIzqZ3394Q2p//+efI2P6NIYuWcwb599A56Tp\nuMLNtG0Voq8ldAIdUVxfyhuTfeGFYk0BkBjUF6WwkGHqGi69eyoTzxyIL5k7PmvaDUi/XE3C3kRG\naz8kyUWBdzlNGfkU5e4ct/51kSSJk33/ZlC1GBEmFCeSxYJkaAfV0k/EQ4xrPJlzN1xP9fYE3JGP\nHI+CoeNwOZFQ4Uui77Cmry8YaqG27wwsaoRyu3Cd9f/v8wDEqkWYsZy09DHgohEXcXzZ8bxV/Vaq\njW4fv9VixvHvDlP0DxHd4YlaMIgWDCBnZCDJMp5Z55J39dUU/164QpQSEUuvtgjLPrhgAR1PPkls\n+3YABrzyMkp2Ng2/EeGdfR97FNeECXQ8+a+U20dKGsW7MLR74LF7mDg5XZPUojuwlqRj/A1dJ7pp\nE5kzZiBZrYTmfsqNl1+D15l0z1jFaCNRW4ctmWphb2SccMLur+csES0UbHYQD3lxKA48SVfGG39d\n3cPa9yeH/3FbG7aEmw0V6QpSG6qqUXQb/Ybn47GL0YcrOX/69lVH4Th6PHMrWqj3Rrn/k0qu+ayN\nHO8WmhsTqAkNb1sYwyDl9ulGttn4uH+Ax28YzuneP2EpKiTR3ExmrgOLIuMPCxdcZvZodGs+Vnsz\ncvKRs+liZFKUtX+rcbsZ8cJzDL9sOnkd6yhr+hQAydDgYLp31DADu8TIp8FaBIYOugXZ0HC63IAK\nko2EGiOa0JAlkGWNR5bexx/m/gpfqBlVcZLt24qxchGW3FycY8eCotD617/SdNttRJeKSnKSbqDI\nCpOKJ9EZ7aQrKoyAbtFPaGa+/d3xlURfkqTTJUnaLElSlSRJN+1if39Jkj6RJGmtJEmfSZJUtsM+\nTZKk1cl/b3/53N6KvKOl7w+k3kuSROENv8ZztghtVPLzwWIh0dyMYRg03XIrrff9jfaH/oHkcmEt\nK8M9dQoksxk6Ro0i59JLgLR1JClCKA3X3hN7TR0/vsf7YRPSFnt03Tq0ri4yT52BY9Qoohs3ku/M\nB7tIApeTJYtOrKsLa9+eIZC7/R2cTnIuvhjPOWfvtE8pKIC+fQm32EhERPTM6In98dmF2+TtN7/g\nH/e9TigUoWqB8CEPyXwd1RLn/deXptrZtFmEoWYUFPHMF0KIXVHRYayJbOGm19ZwxVPLufbZFQC0\nunLIC1Sh6RJNVT58raLGgCs5QS1nZVF06628t/U9fr/w93xsqwJJIpBtS43AAPzRTmwJg9YLb6b2\npz8l2yW+A4aGRY6Tn2HHphw4u6v/j77P9Gkyx/1cFB452JZ+PB4iOyJ89L4JP4esMnRDQdJVbE4n\nkqRhSFZiMR/huIbTauHFZffz6Kanea3uY2aveRxVcWJRIwQ/+4zs876HpChYMjLQvF68r7xK/VVX\ngaFjCQoDo9xTDsBWn7jXawJC9A/6orRvEXu94yRJsgCPAGcAI4ELJUka+aXD7gOeMQxjLPAn4M87\n7IsYhnFU8t/OT3YvxZLMrJhobkELBrBkZO7yOMliQSkoQG1uIVZZ2UNUHEOGIEkS9iGivKCckYGS\nn499sHgfqxbWrieZrEwq2Lvo9/GUIv24ilWlHxNVQpw0fkpqX3DePJBl3NOmIWdloicXZskOYTY7\nLXoqouWrWvoAxbfeQulf/7rLfcrYMUQ7rWhhP6gxJrx8IdO+K4S7fk4CuSqbR2+fg7VehClOYwNh\nVytaMH1rN2/3krDE2NwQRFo/B0hb+n57jNc2LcDqWcbGllZkZw26EsFviN+sszGUSq2QO2oAZbNn\nM3ThAnIvvogXK17sca11jjCJlhbUzk6abruNYY8u56HHNFBVIstXUJKdXKCU6CJkc1Ps2dnFtj9I\nkkSf639B7hmnifeGhsTBE/1YIoJDFQaGPxAGmxvDsCLrKjaHHUlSQbISjfmJJDQcjiCPb/4vU8MR\njgtH2OhtQLU4UDTRyXpmzQKgzwMP0OeBB7DkiL+xrKsk1zRSni1E/7IPLqPaW40vJjpW09LfPV/F\nzJgEVBmGsdUwjDjwInDOl44ZCXQvwfx0F/tNvoRj+DCUoiJ8b74pLP2sXYs+gLWoiERLM4E5HwKQ\ne4XIl557+WUA2AaLiVY5mXjL1q+vGBLfcy+RDRs47vX/kNP2JDNPmLJz47vguinXcOv11zD2tw6y\nnemOIrxiJY4RI1BycpDd7pTo23LFRLTqGEg8ud6gO6na/mIrKEJLyOgRH3hrIdDE+LUPE7CnJ1kd\nwXTUT54RRFfCyNG0TzfWLBH2dDG2/iXulZ8DwJ0U/ZADXP0fxVH6Gra8z3AP+CcFg59mRVYJFjWC\nt8mPvyOCRYvhHlBK5sknISkKa9rWsLptdY9rXa5vxYhGabv/fryvvErJRh8teZB5igjPHOQQrhxH\ntJ06I4NBBfvvz98TkqHBQRT9eCwd4hr2xyAWwDAUZEPF6rSDpKNLNmJRH5G4RknOk8R1lRtHXsn3\nC47BFQNVcaCoQvStpWIFrnvysWSdfhoDXnmFkrvuEp1Zsh5wibuELJv4+//qs18BcHTh0Ye8jsDh\nzFcR/T7AjjGA9cltO7IG+F7y9SwgU5Kk7ulzhyRJyyVJWixJ0rm7+gBJkq5JHrO8rW3vWQqPBCRF\nwfO9WYS++AK1pWW3lj6AUlxMbFMFXc89R8ZJJ1H461/R//nnyDpdlMfrtuzdU6em2naOHYvW2UnD\nL36JbOhML8ml0F2428/4MgM8Azi1/6mp94auE12/HsdY4bOV3W70oHDrDDguB7+9nbxxTtRmUVSk\ney5if3HkZIMhQchLoFlEcATjCqGBu67AVP1SMa54AFtyoZaqqjh82diLdRzhRlyGgV03cCfDOuO2\n9LxAQakIC43KtawsHIYz0k7H2q34G304op04ysuJqBGWNi3l4vcvBmBQcr3EiFicz4bE0Z12vK+8\nin3ECGbfWsqL50H+z34GgPXpxUSUABnhFprlDC5NZjT9ppA4uO6daCwdKxsNqjDrUTTJKdw7DgeS\nrGPIVj5p+JzWaCWNznrOMzIon34jJwyZxeWtEQzZivL/2TvvMLuqcv9/1i6nTj3TM5PJpCdACIHQ\nRIogGEDkWjGIIhelCV5BFP15rwVB8YqiKHoBQQWRYg8qKAgEpccQII30NpnJ9H7a3nv9/lj7nDOT\nZJIJOZOEmfV5njxzztrlrL0z8z3vftdb/JhbIzR0vSNQV0v46HkYnoOUKjjBEAbPXvAsx9ccz8bu\njZSFyphfNf+g9xE4lMmXQ/F64FQhxKvAqUAjZGufTpJSzgcuBH4ghJi688FSyruklPOllPMrKiry\nNFjHT9wAACAASURBVKVDn/ARRwCozNPC4UU/Mn8+XjKJGYtR8V+fRVgWkWOOyW4P1NVSf98vqf7G\n17Njk+6/D6umhvR2JY7Wft7X1MaNeP39hI9Qom/6eQYAHzj6PE67YSLnH30OTmcnmGY2XHN/CfhP\nQGZvBz1Nyl3V5dh85KNnsGTKX+g8fiX2WUMNhVh3F7YbYiAeZ/m6dVieTXVDCSWpJgbMIm5pbSPq\n+/TPTapreGftO+lJdWfPsbxeYIc8upv7aH9lBcFEB1/d9n8c98BxXPr3SwFoSDmc7Jd8Pi6RoL1I\nsOozZ2JPmEDsoo/RSZwiTILTpqmIpx2dlAVvYdq6v5AMRjhm0tA6NHlHuiAOoHtnUDZ4vCsNU04l\nZRRieC7BcBBhSjzD5nvL7+F1biQtBOfNvICOhx6i4/H1vLdXJZllLP3dYQQCGNIBafKvtWptxzRM\nvnTslzi59mSuOuoqbNNGInEPdN2htwkjEf1GYPCzep0/lkVKuV1K+QEp5TzgK/5Yl/+z0f+5AXgG\nmLf/0x4bBKfmvv/MPYh+7OMXMevVpUx97K/ZCpU7Ez3uuCGWkTBN7OpcJm3Xgw+x+RMX7za7F8hW\nwtwdPU88QduddwIQ8r+ojGgUb2AA6XkIIThxwokYwsDt7MIsKdltTP5bIRPlNKv5CWqfU5VDpbCY\nU304P//i9/jvS67msg9cQPsRbxJIKA+jjQrf3LSpideWqbDJKdMbmCBb2FJxGsdZE/l0ay8iEuQL\nnZ0srr+AUyz1YGpJSX06TXDiw7TMnEk8VEY8VE6kUPByefeQuf2qqZmQq8JDbTdAqevycmU70576\nB+mzT2GV20eRYSMCAaY9oVxz523dQsDpx4mOrmsHlKWPNIf9P883g0U/2CXp2jGA8ASG9H36psAz\nAwT8gDBbSqo2lNL0zW/RfP9vaUmotYiMT3+32DbCcxDC5AdP5sKYM5VnPzLzI9iG30lOW/u7ZSR/\nma8A04UQk4UQAeCjwJAoHCFEuRAic64vA/f646VCiGBmH+AkYGW+Jv92x544MVuWYE+W/lul/OrP\nEDriCKxK5dYZePllBl4YWpY43dxM2//dyeoj5tDygx/sco6+xYtpvOaz9Cx6FCyL4OQGNV8/kcob\nyP2Btv/sZ3Q9/DBm6f61/xuM4VeQTKdz5QqK2bVJ+iUf/CDnNqsWkyGpCps9/cNN9D0Tpaeglc54\nhErRhRlroOj4j+OlDMzCIuyiWmKLv8sHFv+IbyVD3NPUwoU9vQirj3VmP9KwcOwI8QUziAeHljQo\n9jxmGidwWWc3h1snUeW4NHaqhezbX70dpOR9hroXVk0NIhwmnvA9o9H8/3/vjEAt5B6o1oLpQf1w\nrXQVD3ztRQymIzwHMxgAS335hNNKlCf1emz7+e956eSbeeGkm2lZ6T+VOnGwdl+ewggEMDwXA5MV\n23tw3F0zmjOir/36u2evoi+ldICrgb8Bq4BHpJQrhBA3CiEy0TinAW8KIdYAVcDN/vhsYIkQ4jXU\nAu8tUkot+j7CNHMNRQrzb/kVnHQSk3/7m6zoA3QvejT72uvvZ91p76LVF/uBF17c5RwD/16afW2W\nlOS+pDKi77t4pOvScuv3ALBK8ue2yDwBXdT/ZZ5wlUur2N21z2pfd2s2+7MoNdQijx7m8Oe/PY7n\nCGpEMRx/BU7p0VjVNWCpCJqghPO2r+HoZJLJaSUWTyXWUDurlMpJhayrHPpleXgyydIPvUCksJpr\nurqJBIspdySdrvrCWdL8Cicn05wWVouRwjAITG4g3u4njo3Cl/wu+O6dAyV+qZQS/fK210mIDdmy\n4UI6CCGI2ErIz+5U+52/GNaUnkrcKMQRQdZM/wgAxScdR8PDD+3mE0DYtnLvYBJPu6xv3bV3smWo\nz9GW/u4Z0TO4lPKvUsoZUsqpUsqb/bGvSikX+a9/K6Wc7u/zKSll0h9/Xko5R0o51/95z+hdytuT\n2u9/j6Jzz91jktL+Mtjtk1iT63DV89hjQ/bL+P+HG5OJ3ELdzqKfXJdLhsqE1uUDwy/C9v6ZJXw6\n/Xn+N/0RogxAeqgLIN7dhuco0a+Ib8fDo2XWKorf3837/+O9fDp+DzvWV7D1f+6h5cc/Id2ZwK6Z\nAJPU4jfn/0T9rD2GhmqVq2Cam6k4p44P3jCfVS3/ZloqxQd7+/j7lkbu376DubNnUXjKlTzpziNy\n8lUUukE6RJKWgRa29TUyr78PJuf+X4OTp+C2q3wCt2z0164EKnonb+LXv+fG74mE+pya5hfoS99G\njx9hFUyoNRezWIXxWhuPpbZNUt8yh5aKozn23AZmHFtByo/Cqbn6csKHH76bTwARCCA8B+lHJb0y\nqFRGhgNp6buey23/vo3m/ua973yIoDNyDzKhmTOp/d6t2Vj70UBEci0QU+s3IP16t73/eAqjqIji\n88+nZOFHcVpb8VJD2zamm5oIzlalBqInnZQd31n046/mwhdFOD9ZppAT/fOnl3D/pccRjal1Cnn7\nPNr6kjy3Ti3mxXvacZN+BcZED23nlPCNz32Gi97zfjY0tjHPWEcffr/V++4nvX27Cgk8+7vw2WUw\n72PwxY3wiUVUH381Ic+jOLiF1S19GIZg68B2Dkum+HpbBzWu8pWbpsGc6VM4/RtPc9TsmRQQpd/w\neGLzEwDML50Nx1/Gmh29rG7uGVKaQlbvHAA3GniAlRfxS29+np/fNZf+Nx7Z/Q47VpL2++EansPM\n5ElkHL6R/k0A2bLIp7+6nu/f7bKl/r2UxkyOObuButm5WjnByB5KKFgWhnTxMJlZVcivX9qyi4vn\nQPr013Su4d7l93LDszeM+mflCy364wAjnCu9K5NJ0n4VzsTKlRSceioTvnML4Tl+l63moRZLevt2\nQjNmMPkPv2fCt27OnXMn0U/5ZSEAvL5dH7nfKplyy15fLyfVF3H63b+la0MY0dvE/7v3L1z7s8e5\n69n13P/0azgJ9escdNJMXf+b7Dla31TZuVZSPfbLgQFkKoVdUwOBCMR8MY7EIFiAUT6TGak0RmQ7\nW9r76U/30ymSlKctXvOmcOek21j2oedy98JQTxhTUV9It7x8C1WOw6yGM1i7o5ezbnuWBT/4J/c1\nqv367RDBWP7WPYZDiDy5dzyP3zx3M9+PlXLfa3futM2F7a/CT09EblYZzcJzCPYmEb4PP9Svft9s\nX/RfnftfrJ55Ef0FtRx51mRMy2DibNVcxQ6ahIt2raKauyahRF+afPqUKaxs6uHaR4aWV/7Fc2pd\n5UBY+nE/0iiTEfx2QIv+OCDj3gn6kT+JVatwOjpwduwgdJhKrrYnqLj69Pam7HHScXB27MCaUENo\n9uwhVTBzoq9i9d3unB/d6911ofUtz93/HLevL9tsZdUbqsTDT9ov5eXQZ3jq8d/zU/sHuH67xH43\nyuz+XBkGr1GtSzjtvRS97zzsOnW8XZtrvzeE0kmcmEjRFexkfUcrm3o2ARAyJlH3xRe5/JL/5Ng5\ns3c5bFJ4Gpd0KZ/+OX0DfGpxkDNvyzUqf7RTiVlTpIyiyPDClj888uHeWf/45/lxYhPg16sfnO36\n+Jfo+dm7WFA3gfXCX+/xHOzebp6f/TvEwN+xU37zG78IVDJUSlONShSceoz6oiwoDfGp207hP7/7\nTuzAnsNMDengYvHBo2v54NF1PLO6ZUiE0opGZXQcCNHvTKqaP13JXdeZDlW06I8DMu6dyNFHIyIR\nmm+6mcb/+hwAId91Y/vJVKmtucYgzo4d4HnZbYMxourpIWPpuz09BCZNUqWJ//sr+Zu7bSPCYbze\nPtJ+R7FIaYxeIlhCPdY/FLgJ6Qqkpyxpxw1S6Kg/RiklpV3LaTOqcVpaCdRNpPh8lTCeyfjcBdPm\npGAlUsCG3tfZ1KHWK2KRKZQVDF86QVbP5brOLh7dup0LOx36K4/hhgWzePHLZ/Dil8+gsaAcKQya\no7EhJYVHC4GXF0v/7raX6DWVVCyzBWzKdbN65fVfcuGEahptizf9Gk+GdChub+adj8apbP4T0q/o\nOuPwhiHnNZ1+woW5L79g2MLai+ADGCj3jhCCo+pL6E06NHWr9abueJpMFvKBcO9kyj4AJHbXyOEQ\nRIv+OMAIKkvfLCnBrq7GbW8n3dhI0bnnEj5qLqDCR62KimzXLoDEWhXjPjifIEPG7ZJZ6PW6u7Eq\nKqi/805CM2fmd/4FUdyO9mxzmKJYMR0N5w7Zxx3ULUs6BiWyC8f12NGTZIa7jh6mg5TYdXXEPnkx\n1V/7KsE9zHNGvVqA7UltYvFyFdVUUT5nj/M89qyFPGceS4PjsDb6Dh656hSuPG0q1cUhqoqCBCNh\nls09lX9OmHtARB8hkcLiwr9cSF+qb9+OHcgtkDY5/cwXUb4w73NssW22rsj59T9dXclmW12L7Sj3\nmWOZlCe6OW/VOg7fAq6phN2qqBryEZb71lpFGnh4mLDiD8yoVL+Ha3aop8vVTT1IPwv5gFj6iVyJ\n7+19u88SP9TQoj+OMKIRYhdfDMCkBx+k9nu3Zl0/wjAoOON0ep98kvhrykea9NsY7k4czViM0JFH\n0nr7jxhYuhS3pwcjT1m4O1PwjnfQ/adF7PjmTf6FGEz68C30TT8fWaCExE35v8pCgCMoo4eO/gSv\nrd3AJKMFZ8kARkEB0ZPegVlYSOnChXtsSB6d/h5irkuZvZGlW1ZQ4rpU1c/d4zxDAYv6K3/PA5Nu\npui8bw3ZJoRgakWU78z+D56tO+oAib6HFAbdqW7ufuPuER/W/eTX+Nmdc+le/huQkiaZpiZYwumT\nVfLUk22vQc92SMcZvIRqu0r0k2e/j9rbvk/cVr9brh9zXze7lOPOm8xZF6ughUB5jLeCYRp4IgjP\n/4gZVSr0dV2L+lJb3dybLSd9ICz9DR25Hgvb+7Xoaw4RZCYd3TQpveAjzHrjdeyqXevwxC66CCMU\novG6zxNftozEqtXYEycO6V2bQZgm9ffeg1VdxfYvfJHkmjWYRaPThLvi2muxa3PRLm5HB0TLKfjY\nfYjPvAxfac6Kvl1bC2kPW7hcfMffeeOlp/EcgbuumdKPfQy7qmq4jxlK7dHUpx0KCzoJBHuocVxm\nz9qzpQ8wsbyIj11yNXNn7RqNNbWiwHc/wISS/EU4DUdIGCAsYsFSduzUC2BYUv1cuvFhfhgr4Q8r\nH8Ad6KDFNKgOV1BXWMd0I8KLyRb4/mzkzdWEBvnSM6Jv1FRTdPbZdJYrt6DwnwRM0+DYcydTOU1F\n6lTPHHktqMEYBnhGAJrfoDQI5QWBrKW/vrUv2y3sQIi+tvQ1hyZ+SJswlAWU+SPcmeC0aVRcdy3p\nxkY2fXQhvX/727BlH0C5eOp++EOcNr8GyiiJvl1dzbR/PEnZp1TNG6dDuR6k5+ERBDuMe+Zt6hpm\nzUT0xOnaGOaqgZ9S2/QEvV1R8CThuXu21IcQiTHREyQC/dgFCSZ4YIygH8GemFqZ+/KcGIvsYc/8\nYAiBFCaxYAlJZ2SulK0rf8+bAfX7saRnI62ty3GFoKqgjg2vtlJvlvJ8JMwVVRXcUFFGfFC5DdtT\nom+FlDsnHVBrSUVHDb3vxRURzv3MkZx20fC/W3u+LokrLHBT0LKC6ZWFrNmhLP2Nbf0ghyZnPbT6\nIebeN3dUavF0JLpwE1VIaWjR1xw65Cz9vf93R08cWn45etI79rh/eM4cgjNU43SzZHTcOxkqr7+e\nis9fhxwYILVtGy23fo835x1NfPkKGr+swkkrr70Wo7qcni1hzjNfZKH1NElDRSiFjth9ws9wTAwU\ns8NLssHtZ4IVVa6j/WCaL/qlEZui0AFYyDUknjAJmUHie6pnM4hn16rWg/NDVSw1HJ763YUAhNvn\n8tidbzCx+V0APBcJ85hfIuPOd9/JJd0LCaeVq8UOq4XbSaep3525n71sl89pmFNOILT7Ugt7wzTA\nwz9247PMqCpgXUsfUko2tPYjfUs/49P/9svfxpMefel9XNcYAT2pbqRbgEyXsK1Xi77mUCFr6e/9\nvzsweTKFZ52VfV9w2ml7PSZTwdMYJUt/yGfFlGtg/bvPpOPeewHY5PcVBrCqqglPqsf1Y/apOYq0\n14BVVYVduW/uhDMKcslU80PVe9hzZGTq59eXRfeyZ34QAhAGQewRW/ov96ynFouPzFxIr2nw7fIY\n0WQx7a+qSKdwZ8OQ/f9n7tcJvjGB4MoTqO87G4CgH4466/rPMu2ZpwnlqbdCBsMADwPqT4RX7uHw\nmKQv6bChrZ/Grji273LKWPqeVL//PamevM4DoDfVjXSjeOkSNvdsy/v5RwMt+uOAorNV3f3BTciH\nQwhB3e0/ZNKv7qfic58bUqlzOKzycvXCG/1qjgWnnkLB6acPu92IRrAm1OOIMrhhE1y+mMS6bYTm\nHLHPnzWj/HC+1N7FZ7t6OT22b08Ju2NSWQTLENQfANcOgGGqJ5OwFxxROKEc6GSZSHJMtJ6TZue+\nSK91b2agQ1nNsr2MgJPL8BaP1fPiH4cmJtlRZekL2x7R78++EjAcPGGxofrz0L2Vs1d+EYA7nlKh\ntQ8GVBe2naN3elP5yx/JEHd7kG4E6YXoTeYvKXE00aI/DoieeCKzV6/abejlcETmz6f8istHtG+m\nhISwRr92u1VezsSf3EH5VVfudrsQAqu8HKc3gQyV4Pb0kNq0Kdu7YJ+YegYf6+nh052dULMP6wHD\nYJsGNyyYxceOH3kryf0hUwYh7Ngk3L2L/pZN/6DDNDmqej5FwWKOK5nF7IJ6AgOFlFRF+MAXjgHP\nYFLnYUQkPLfwOVo27ySk0iOYxzIcu6NBbKDA7WTpG6Vw/BUUtC5lenmI37/aSMAyiEnlxtl5ITff\nou9Jj6TsQ7oR8CziTmrvBx0CvDWnmkYziNILF2KEQ9mkpwNB2RVXYJaXU3DKKZgFBaw5IbcWYZaV\nQTqN191NYqUq6ho6/C2IfsNJcNxlkOyDuRfmZd6fPmVKXs4zIkwBHoRci4TYu+hv7lYN5KcWH8aq\n55u44z0PYAUMHvzqy1RNLiZVZBEoNJjUeQQ9EzdSFCjCDpqkk7kFUsNzCITz2/t3ZwIBQUXvJrZs\nLcM943BMJ8H/nBTh8r+mueiIENZa373jDrX08+3eUV8ikvJwKZ1O94hdaAcbLfqa/UaYJiWD/OoH\nAiMQIHZhToinPLoI1y//YJUpd1PL976nMnqDQcLz3mLvnnO+u99zPVgI04A0BB2TpNi7IMVTyj2x\nelEpnVtX8c74dI58Vx19nUmCE5M88olrKDn+Yiq6JnKBvJotK9uHCD74JRhG2dIXtk1RvBE3Oo92\nOYNK4JSSdl7/+jmY65+i1e+tknCSJAclgOXb0s+EaxYHSkiKIGnv7WHpa/eOZkwQnD6dyNGqJLJV\nrhZ7u37zWzp//SDRk9+JWXBgFk8PKWxlcYcS7oh8+ol0H9FkMZ1+R+zta7sY6E3huZIdS17ivRtf\nwGjaTHGigtQzZTx6u0riO+XCmch6tU5hSGfU3TsiEKB4QE1yW6tfxrt1FbZpYLSuxPIXcpOuQ3s8\nVw4636KfqbdTHCymIBDCkW+P+v1a9DVjDjOWK9MbmDqV8stHtjYx1nCDKloolEiOyKcfTw9Q36UW\nrCsnFbJ9bRfbVitr9qiVqhWl6B3aj7ikNsqTmzYwZbFfmkHK0Rd92yY00EbNtGKWP9dKomAWtKjs\ncdrWIKSN6VkknTSvteYqcObbvZOpuxMLlxK0gnho0ddoDgrBGdOpvOEGpj//HFP/8mfCc/aeSTsm\nCfrW94rzkGmx1+SkhDNAXdcMoiU2R76rjkR/mid/rtZEIgOq3EBZ26Yhx6xofIYF37qMcFwl6KWC\nxQQioy/6Mp1m/tkN9HUmebz1amhZpTa2r+N3zfdw0b+/wcbuDfz41R9TW1BLYaCQnmR+Rb/Dd+9U\nRmLYRgBwDlg/4v1Bi75mzCGEoOyST2LF3lptl7GC9DOwDa+Ik9efOcS/vTsG0nGqe6dQM62ErpLc\ncl/llj/RVlNJ+rovExvUIer8a+fx8Wd+AUBZR64LqhUc3bLRIhBAplLUH17GkafX0dxbg9e2DlwH\n2bqWlIwSdgr4y6Y/sKV3C5875nMUB4rpTefXvbOjT2WGVxaUETACIOTboi+vFn2NZowyc7JN/Za/\nY6d6md5UslcXT6IvSDRdzLKtW1h+5VXYxQZFFWEmbH8Rr7yK2R96L5bvF48WB6ibWYrjf7HYTj/C\nczGdxB4L2eWDjKUPUDahANcz6EmVQeO/Gegb+jQzo3QGCxoWUBgoHFIGOR/s6O9ASoPqgiICfiXR\n1NtgMVeLvkYzRpn1oQWc9qEottuL6QX2upib7lVrAEc/eS/vaFpOuPMJzrrmcGKJHsyaGuxYjO5S\nVX+ot9DgkZc345IT+JOf+wLzXrlx9C7IxywuRiYS9DzxBGW1aoG+3amHP3+ONmfykH2Lg6o0SENR\nA2s61uTV/dKbjINnUxoNKksf9vo0dSigRV+jGaMYgQCF//k/CJnCYO8JWum0EvAS31dduPo1Nq/d\nBECkTlU59YqjzHv1Nt716yuZ84kFBD2H+IzDSEQL/fr4+S9qtjOlCz9KYNpU2u/+GaXVSvQ7Ck+B\nlpW0G2ohOm2qBK3igBL9eVXzaIm35LX88UAqieEGaH1yO0VJVXco5WpLX6PRHGQMmUKwd0vfcZXo\nCzdN20nvZkrbJpZ/RVnuZVNUFrGoqKC0ex3mIDfGtG/dSPM7zlTnMEY/K9ssKqLg1FNJrlqFJVyK\nykN0FJ8OM8+ls0zVjZKGml/G0j+6UoXzLt2xNG/zGEiniMUn0PpGB5NfVy04d07QWtayjIH0QN4+\nMx9o0ddoxjiGdBDYe3U9uI4S7O6ySiYv/BC253Ji8wqcUJhpJx4FQGCnPgxTnn6aoiMOp7BW1c6P\npA6MwIXnHIlMpxl4dRmxiiAdHQYs/DVdjnoiMT0lbUVBVQRwaslUTGFm+x3ng3g6he2qSKVAQlVN\n7Uh00+ZHMvWl+vj4Yx/n84s/n7fPzAda9DWaMY5BGkSAuLPn8spuWsmBN2sWtafmSmrPXvw0th8J\nFZmQ65cszj6PYI0qqHbY2acBEHIPTKx6eO6RAGy5+GJ46o90NQ/gOh6dO1RWcTChoo+KAkr0LcOi\nMlJJU19T3uYQd1KE0modxPTv3Q+X/oCL/noRkEsGe67xud2f4CChyzBoNGMcAwewhxf9dByaXiPc\no+Qgdtw8hG1TcN31yFQaa1AbzKI6JfrdoUJOuO1/s+OlRx7OCHtz5QW7pga7vp70li1EOjfh1Uia\n1nWR7HdAekiRE/17/7WRG/+8klNPraGxrxEpZV4ijBzPocDJlRO3nSAr2t8g4SZIe+lsMpjk0Ird\n15a+RjPGsYSDNAJs9Auq7czaxTfxxUcvxEiaGG6KSIOqfz/xskupv/qKIfuWT1SWvRkZWh5aCEHt\nXXdRe/vto3AFu2fCt1Uf4pLudQgBT/5iVfa9lwklNWy++ReVQ1AeqmZpy1I+sOgDefl8V7qEndx9\nKEqWZxfL2+Pto9K0JR9o0ddoxjiWcJEiwKr2VblBKWHjs+B5PNy2lMcKorheCMNLESrctSdyhspZ\n07DOPpfD7rpjl21Fp5xM0VlnjsYl7JbIMccw+Y9/IJTsYkKlS39XkmgESrrWI4VJxsDORGlGLRVu\nuq5rXV6SqFzpUODkso8Lk7lkwCc3P8lVT16Vfd/Y10h/+tCot69FX6MZ4xiGh2fYrO5YnR1rX3I3\n1z12CS3P3UrCD9G0PBvTTREqHL7Ji7Aspt92K9F9bD05WgQmTwbT5KjCtRx+Si1zK5sQ0gFhIDCY\nUpIrZW2Sa/7S2Ne435/teg5hJ0JAKDEfLPrfeeU7DDi5Re0Fv1vANU9ds9+fmQ+06Gs0YxzD8JCG\nxdburVm//s9XPcAT0Qgff/Pn/MkXLcsLYnopIkWFB3O6+4QRDBKYNAlz00pOu3AmVZ2vY/g1hr5S\n/VXmVuSa35xc8SEuOfwSADZ0bdjt+fYFD5eQE6HE3I4p4hQm9lz245XmV/b7M/OBFn2NZoxjWMq/\nEXRsdvTvYEX7Cv7oqrDC7bZa8DzTimFKZelHioZ37xyKBKdPJ7l2LQDJDRuVpQ8UMfTLK54y+fSR\nnwZgY8/u1zf2BVc6hJwwIaOHqNkyxNLfHdXR/LeOfCto0ddoxjiGpSJVIskATf1NfOP5b+BJONnM\nReUEIhUUihoMN4UVGt3OV/kmOH066S1b8QYGSG3YgOE3QpepNAOpnO++J5GmMFBIoV1Ii181dH/w\npEvQiRA2eoia7URTJYTMXfs2hC3lVsqEjx5stOhrNGMc0/YbpKdtFm9bzKqOVVzd2cWPGz7Esxc8\ny4LaU7n0lJvBM0E6o14wLd8Ep08HKelbvBivvx8hlOi7qRStvbmEtO64yiGwTZt0HvIJPBwCToSQ\n0YstklieTXlwAtcdcx23nnprdr9F/7GI2bHZ2aYrBxst+hrNGMcMqD/zSCrAA6seIGyGOLd/ACNY\nQGmolOunfYuV9w/gyiDibVAaeGeixx+HCAZpvPY6AIITqwBw0w6L17RSTB8nGiuyoh8wA/mphumq\ndZBQMI1NClNapBMxPj77Yt7T8J7sbm1dYU6oOYGuRNchUW9/RKIvhFgghHhTCLFOCPGl3WyfJIT4\nhxDidSHEM0KIukHbLhZCrPX/XZzPyWs0mr1jBFWJgGBa/Xz/xDMo9jwIKN/9queb2La6E8+IAG8/\n0TdLSih677kABGfOJFClfOtrGzv538ff5OGC7/Ng4GYG+lXcvG3YpL39t/RtR93PcGEYyxSYnsWW\nHUVc8+CrQ/a78O4XKQmVkPJSe82KPhDsVfSFECZwB3A2cBiwUAhx2E673QrcJ6U8ErgR+LZ/bAz4\nGnA8cBzwNSFEaf6mr9Fo9oYdUuJULtWf3hUN76XfLeF3fyyjq2UAZ1Bzc/n28uxkqf7KV6j+NfgJ\n3wAAIABJREFU2lepuembWLZKzNqyo4u+pMMsR4WqOv2q6UnACOSlGmYg7fcgjpqYwSCFToBU+8k8\ntrwZKSU3HH0z8W0foyfhUBpU9/5QcPGMxNI/DlgnpdwgpUwBDwHn77TPYcBT/uunB21/D/CElLJD\nStkJPAEs2P9pazSakWJFlDgtiJzKkx96klIMtiTn0dxk8sBXX+TNl3LdsBKRCQdrmvuFEYlQunAh\n4TlzsPyIpIDnMqc2t1id7FX5CLb51iz97mT3EEs9mFY19MOFQYxgEEOaINVYX9LhyNJTcHpVq85M\ntc/OZOdbuLr8MhLRrwW2Dnq/zR8bzGtAJrf5/UChEKJshMdqNJpRJFikskatuEVVtAqSfbgMbWk4\n/VjlBw8ltu5y/NsN07f0g55LScTOju9oacb1JAEjsM8Luc9sfYZ3PvROvvHCN7JjIT8bN1QUwrIF\nnsx9VlN3guWNuZ68pSHf0k8cfEs/XwXXrgd+LIT4JPAs0Mg+dFMQQlwGXAZQX1+fpylpNBqA+qkx\nnn/ZpbPN/5NM9dHnlg3Z59VVWzhi+R3IkkMjrHB/MAIZS9+jOJwTYjvdw6qmHizD2mdL/w9r/wDA\n1p7cl2LQUaGYljCxLIHEREjlIvvkvS+zvTvXv+D3r6gvgHyEiu4vI7H0G4GJg97X+WNZpJTbpZQf\nkFLOA77ij3WN5Fh/37uklPOllPMrKir28RI0Gs2eKJ05lejAdhK9vnWf6qffLSNaZHLFHacRtBo5\n88mbmNK2HoJvrxj93WEGlNDb0iMWNulyqnmt/1yK6efVrV0qemcffPqNfY08tdX3Xg9a8willaW/\n9b/vI7VZWfAzK1Sc/mDBB7j/X2r7joEDWYt094xE9F8BpgshJgshAsBHgUWDdxBClAshMuf6MnCv\n//pvwFlCiFJ/Afcsf0yj0RwgRPl0StKbSYqYChlM9dHnxegc6KOxK06s6yXCCbXI6QVDeznboU9G\n9APSo9oe4I8dN/Gv3k9RTIK+hLNP0TtSSj686MPZ94O/LGw3ACQxpEdqi7p/P/v4/GFOZCGd6NtD\n9KWUDnA1SqxXAY9IKVcIIW4UQrzP3+004E0hxBqgCrjZP7YD+Cbqi+MV4EZ/TKPRHCgCEQqtFhyr\ngLa2fkj10Z2opLZpNbde8U2SmzZld413dh+8eeYJI6ieaGwpmeg10u8pV1aR55JIu/sUp79jYAe9\n6V5OrDmRE6pOYyCds+ANDDIhrrJfFVcrCQzvMbcpZfHWxfxz2z/51kvfOmj9dEcUpy+l/KuUcoaU\ncqqUMiPoX5VSLvJf/1ZKOd3f51NSyuSgY++VUk7z//18dC5Do9HsiVCx8ktsXrGVp18M02tMIDLQ\nwpVv/Imj2tbTUa7iK+pTB3+hcX8xfRdV0HOZv/Xe7Hix4ZJwXGXpj3AhN1OZ9LDwh/nnmz3s6M3V\nyDc8A+EvXRr++VxHUhC0MI2cH+gb71MVSYVXSGu8lav+cRUPrn4w5zI6wOiMXI1mHBCuUSGDLSs2\nsmbtJIq6NzBpy9+z2ztrJ1N01WeY+8PvHqwp5g3Tt/RPFiuoac21KixEkkx7++TeWd2xGoFgydog\nUlo4/hOC67kYmBh+cTfDz2R2U2kWf+E0VnzjPZgSvtAVZk6f4OITJ5EWQxdxn9j0xEHJ0NWir9GM\nA2LTVJvDrcvjOGaU2am/MvPxRZRefjkAh519KrWfvZrIIVInf38wQ2pdwpUWA8FcI/ewFFn3zkgt\n/Q3dGygP1fCvNb3KLy+UuDvSwZAmwlN1fjKi/8ZTW3j5V2sI2SYhX89fWrSB0miA+I6hKUp/3/x3\nfrPmN/t1rW8FLfoazTigau7hCC+NI0uJDDQz+8Q0gYkTqb72c0x/4Xkmf+LCgz3FvGEGlV/dkxZW\n+fTseKkXJ5F29ylks6mvieYOv/mKtPFQx6XdNIZnYkjfveOfb92ydja93ob0JJV++QvPkcSiAZze\nOXzt+FsAaChqwDIsVrav3P8L3ke06Gs044DQxNlIQ4nQzM4/Ez338uw2q7QUYYwdKTADOdFP2bkM\n46jrEIi37dNC7rbe7XipEi45qYFpFSUg0kgp2dK7BUOaGJ5LYOpUv/k8JPpdPE8S70tz38ePzZ6n\nNKJcThGjXP20I0wpnkJH4sDHtYyd/2mNRjM80Qombf4btY2LmXvY6zBj7FZDMX0L2/UsdiQnZ8cT\nTgGT+peNeCE37aVpT7ThpUv44NF1BAy1QBx34lzw5wt8946LVV5OoHBofkPT+i7+/Zdco5bSsA0S\nio0G3jf1fdx00k2UhkoPiujnKyNXo9EcyhgGp0z4BUiIfvjbYI7dP/2M6HdRx5q1cwkmOnCsMHG3\nkKrEJnaYh5HyUkgp99g7oHWgFYmHcEuYXlVAwAyAQ9YlY0oT03Uxy0oJDs3F4vE7lw95/9r33+DM\ngE3ngMvN77wZgFgoxoq2FXzk0Y9wQs0JXDf/ujzeheHRlr5GM04o+sBFFJ31bjj6kwd7KqOKFVKu\nlLXGGXT3RZi2/g/Y6T763FLKnCZs383lyD2XkW7qbwKgMlxN0DIJmsqaX9qyFCDr3rFipViDyj0M\nx1Epi+ZBmbqxUIzm/mZWdazi5ysOXDS7Fn2NZrxw7vfgow/AGPLf7w4jYGP61TDLYimqWpdiOwMM\nyBIqnCYCvujvzcXTHm8HoLpAlYbZRfQ9A8N1MUtKMQPmXufVb0iadhL9vDRz2UfG9v++RqMZdwg7\ngJ3uByDkx01G+5volnUcnl6J/a8fAOw1gsfxwzCri1Q9nZClRD9TdM12LYTnYsZiWP7isSWGb5KS\ntgTbu1QE0ePLmxmI50pe1BceuEKTY9exp9FoxiXCtrGdfhKUE7SVcJd0raO5+gQ63Trs/jYIx/Za\nBiGeVturC4eKfpv/BGC7Bob0MEtLMEM29EGx2Ux98FWKZh/D4iWTAFhYfg2Ppu4gFXd5bHkzjy1/\nHACrsImw32OwPFye35uwB7Slr9FoxhQiYGOlVS2cgJ+oVdK9DoDm1CxsPwt2b5Z+e7+y2jOWftgX\n/QFHPUVYnomQDlYslu1OVmI28Y7C+zli2+ey54lZ25jm/J5Q2oFBCbhuvI5SYzqlwdIDWodHi75G\noxlTCMvC9oVZdKkCcqGE6lj1mHsaAV/09ya07QNK9CcUD7X0M5ieCtk0S0sxg0EMN0nM3rzbc4WN\nbjxsMsu90yoLmFk+kVny/3FU5VEH1LevRV+j0YwphG1jeCpTNv3cM0ROOAG7rAQhXVY6U0ds6fcm\nVd3IsgKVkRuxcz54N1mJ6WfkmqUxjHCY+Uu/y1HhXNX5s4pv5axiVcsoZPQCcMWJDQBYhqCsIEB7\nf2qf6/vvL1r0NRrNmEIEAuDH31tunAnfuhmzqAhDpjGkhWUry31vou/6XxwhS9nn4VSQgN8ty+k5\nggLHREgXs7QEEYpQ0N+EFS7IHj89/BzTw8/DhKMJG6pz1jHVqvCdlFAWDdLel+S5tV209vXn8Q7s\nGS36Go1mTCEMA2H73bNqa7EnTMCKxbDcBAEJeEr29mZdp1y1CBw0bdYu2YHzG4dTNnwEAOmFABOB\nhxEIYIQjarx4Mix8GC5bnDvRJ/9CaMpcACr8pu2XvnMysWiA7d0JOvo8epM7ZXeNIlr0NRrNmEME\nVIKWEVGWefioudjJXsKeJO5r/d5DNtX2oGWz+oVmAAqTMbXRDSMxMfx6+iKsnh68wkkwcwFMOCp3\nokCEaLWq9ikGHDbdci4fOXYi5QUBUo4H0kKIEbcU32+06Gs0mjHHTO8NSjtWUVesfOmRY4/FcuKU\npJOYnkqkGvAjfIbD8d07vVvjbFnRPmSb9EJIYWabqIiMpV999G7PFS0rADz62nqyY2UF/sKwtBDm\nnrOD84kWfY1GM+YoLLaY9/qPCZUoH3v46KOxnAQFnkfEVS6WvRU7c7w00jP4192r1IAA01PHzq2t\nxsPExK+n73frklMHFbK7Zqn6B5gF5USMLvraerObM5U3pbTItF08EOjkLI1GM+YwCpXYmyVq4dQs\nLMQ2PIoCNmHXACTtifY9nAEc6VKcqMBzJXPfVUP72tV0tqm1gtmVlUhhYggVCSQyop9M5k5QNjX3\nOlJGgbmeVf+OUVK/mc3L2zn2ohmcNK2M1fEgKeHx9ee/zoAzwP+e8r95ugu7R1v6Go1mzJGpnmmW\nlGTH7LCF4wosgoSkGIGl71DePxGAmdbjFLT9m7CrRL8oGEEKC0MoS18ElOh7g0V/MNFygkL1133h\nD+vZvraLnrU9PPCpE6guUl9QqzpW0RZve4tXPHK06Gs0mjGLEcrF1gcLgqQJIJZ5lHgjFf06DFMQ\na30UU6SJujaJ5vNpKJyizu+7d4Tfl1cmh4kIKqol4RUNGVrxz0ZSCQfbUMe2DbRRHCh+S9e5L2jR\n12g0YxCx008IFoZBGLxU/nVKHMlfNvyFv236G/9q/Nduz+BKl5J4BcWRXszmf2OJJEiLdOeJFPoF\n1oSh3DtZn35qeEv/1NJ7sk8GAC2be1n2xBZsUz09tCXaKAoW7f74PKJFX6PRjDmCM1RvXLumOjdo\nqqgdx45S3amk7/rF13Plk1eyvmv9LudwPIfCVCmFyTcBMCIFeFIJdIFfSjnr3vFFP758OVLmCux0\n/OoB0o2NIARV5XHOnvsMALGqANHiAL3tCdWcBfCkR1FAi75Go9HsM7FLLqH+vl8Sfcc7cmOnHJ99\nbXUMrWqZaZgyGFc6FKZKKDRb4YJfYRWVI7EQEvpWdAFg+A8SGZ9+2+0/ouWW7wDgdHay46ab2PKf\nl6qdiuoo2fY7NZfup4hGPQZ60wR89w5AcVC7dzQajWafEYZB9Ljjhowd/u4pnLFQ9cw9trVqyLYN\nXRv49apfZ2voA5CGkBNVol8zF8tWCh+RsOEfjQCYRiZkMyfcHb/8JQBerwrPTG32i7C5KUqs7Zxd\ncgvvKvoJYdqJ96YoSEYIpdVirrb0NRqNJk8YpsG0d9SD9ChPVrFw1sLstu8u+S7ffvnb/HPbP7Nj\nAb/JSWGoH4onYlpKLu86e07unBlLPzi0Aqfb14/b0ztkjFJVX39K6CUCRpxw92vEu/qZ9a86Pvza\nFwEt+hqNRpNXLNvEcuKkRYTSUOku21vjrdnXwYQS8sJYEITAspVcui25OjnCV9CdRT+9vZH4668N\nPfk5t8L8S7NvI7TR16OeFKJp5da5/cnGt3hlI0cnZ2k0mnGFIR08aVESLNll27bebdnXpqMWa4Ol\naj/LX7zdsaE7t4/fGndn0d9yyX/itueSv5pvvJGCU0+l4IgPwpJ7oGI2YWmDX1wzbaion6AR3c+r\n2zva0tdoNOMKIV08LEqDytKfXDyZf17wTyYXT2Zr79bcjn50pVGk/P+Z5uf9XbmwTMNU/p1MVc/w\nMccADBF8gM5fP8jWy6+Aytlq4PjLCEdz8tsZVgXdSnfzRZRvtKWv0WjGFYYv+plImQK7gO5+m7bO\nAgy25PbzF3WN4gkA2ebnAz25BCxp+vH6QjDz1aXIVIo1x5+w+88tKIBIDL7aAcIg8soN2W2eH/pZ\nFi7L12UOi7b0NRrNuELg4GES9kMlBYJrH15Ge1cBjb05n7rpV9k0SmqQ6TSJNdsB1QAlg2OGs6+N\ncBijaPiFWLu21t/RBCGorXU5p/KHlIeWYUr15VEeLczLNe4JLfoajWZcIaSLJ2zK/ZIHx9Ucx9It\nXUinkITXn22uYnh+OGZxNe333EPnoy/sci5vUIw9KIs/csIJFJx+enas7ic/Ufv29Q3Z1ygsZ7Lx\nDCHimJ5FsvUMSsI2o40WfY1GM64w8PCwqAuX8af/+BOXzbkSAOmqWPlsTR7fojcCIXqffhpjUKet\nOZE/M3nbY0yxd83krf/5vdTd8ePs+8LT30Xs4k/gdnYO3TGqEsTCpJiQMkm1vZviQ0X0hRALhBBv\nCiHWCSG+tJvt9UKIp4UQrwohXhdCnOOPNwgh4kKIZf6//8v3BWg0Gs2+IHBxhQ1OiinFU0g5ajFW\nOipyJiP6Qqrx9l8+TOK117M+foCo0cnUzYuwArtKqBAiW+Uzg1kawxsYGFqF0xd9Uzi4MgAISiKj\nL/p7XcgVQpjAHcCZwDbgFSHEIinlykG7/TfwiJTyp0KIw4C/Ag3+tvVSyqPQaDSaQwAhPDxhgW+5\nJ9LKd+/5ln573I+8kUrQOx/6PSYQqsz564NGP9ID9tDmsPIL1yMsJbFmTEUKuR0dGDU1aoeqORCt\nwOpOge/TLw4HdnuufDISS/84YJ2UcoOUMgU8BJy/0z4SyNyRYmB7/qao0Wg0+UMg8ciJfjylhDsW\nVP1vM81VMpa+kC61P7qdcEOudENADCA9gZDD99ktu/RSYhdfDIAVU+d2OgaVcy6fBl9YR7poAh4q\nHPRQce/UAoOCV9nmjw3m68BFQohtKCv/mkHbJvtun8VCiJP3Z7IajUazv2QsfS+tMmsHfNGfWKya\nlzf3qUYmOdH3CB9+OIGgmT1HoCAKUiCKKkf0maYv+m7HrjX8TRM839I/EO6dfC3kLgR+IaWsA84B\n7hdCGEATUC+lnAdcB/xaCLFLTJMQ4jIhxBIhxJLW1tadN2s0Gk3eMITEExZpv/Z93Hfv1JeUID2b\n7b0Z0TdAuljFRVg1NQQGuV4C01QsvqicMaLPtCrVU4KzY8cu20xLILGZkzRpWXpodM5qBCYOel/n\njw3mUuARACnlC0AIKJdSJqWU7f74v4H1wC53SUp5l5RyvpRyfkVFxb5fhUaj0YwQYYBn2Di+pZ/x\n6U+MRZFOIVt6trKjfwdpPIR0sevr1eJsKEzId/2YAVWMTdgjy2+1qypBCNJNzbtsMyz1RDEnZbJh\nact+X9/eGInovwJMF0JMFkIEgI8Ci3baZwtwBoAQYjZK9FuFEBX+QjBCiCnAdGBDviav0Wg0+4ow\nwBMmTnIn904sjNM3g2Xtz/Pu374bAxMhXQJ+UpUIRzjyjZ/SEHiZkpifiWuNTPSFbWNVVJBu2rVu\nf9h3GzVEQwQPhegdKaUjhLga+BtgAvdKKVcIIW4ElkgpFwGfB+4WQlyLWtT9pJRSCiFOAW4UQqRR\nlSyukFLuuTGlRqPRjCKGKfA8CyehSh9n3DsTSyOku48lEHtR7ecZGJ6LXVen3ociFPQ3cUzxt3nz\nSlWaAXvkIm3VVNP9+99jRCJ0L1qEXVXFhO/dSsCv6ePFXULR0a+MM6JPkFL+FbVAO3jsq4NerwRO\n2s1xvwN+t59z1Gg0mvxhWXiuhdejKmomfEu/tjSMl6jlzLIv8UT7LYRTJob0suUTRKEKu0x25YR+\npJY+gBGJAND5q1+p8/T00PXIbzCrlEvbTXsHxNLXGbkajWZ8YQeQho3sUf7zgZRKuioK2RSHbQrc\nuVw//3rCKQPhuTnRDyk//kBLbkFXWPsg0k4upt+urSXQ0IDT3oZp52Q4tOpXb/myRooWfY1GM64w\nbOVOeX3lWu5cvJ67/7kRIaFtbReTyyIs3dLJ2u2CUFr59K1KFZZpBH3Rb83Vzhf74N6p+eaNBGfN\nUueKRjHLy3Db2jHtXChoMOjt9/XtDS36Go1mXGFaSmQTHa18+7HVNHbFOSlh8ff/W855VTGWN/bw\n4AudGJ4SfRHwq3H6jVKS1GfPNdLoHYBAQwNVN6i2iEgPq6wcp6OD/mUbs/uEIuYwR+cPLfoajWZc\nkXGnnCpX8B3rLv7bup+jUkq855YVUlkYRLoFGNLAkF7Wb2+ElOg7XYOqZYp9k9DAlCkAFJxxBlZZ\nDGfHDnr/tTy7PVhwaJRh0Gg0mjFDNKKE1XEDXGA9w6Xm44T97Nt0d4pnv/guzp49A+GHbGZEX/ju\nHTkwkD3XzuWS94ZdVcXUJ5+k4pprMMvK8Pr6MLxcKYdQQWi/rm0kaNHXaDTjiuJC32L3lJinZc5H\n3906QMg2ObGhntJUACFdyIr+rla429W1z58fqKtFmCZWmeqSZchc9c5gUWSfz7evaNHXaDTjCtNv\ne+h6vsU/WPSbVex+ZWGQoGchPDe7WGuEclZ45ERVhqHglLdeTixTj2dwyeZwie6cpdFoNHnF8pOh\ntpS/G8iJvi3i9LQp1015YRAQCOkhTLV/ZiEXIDJ/PrNXryI4ffpbnkf4yCOJHHssVjR33kDx6DdG\n16Kv0WjGFWZQWfrSVPXzM+6dIrOZVMrASbuURQOAMcSnbwwSfbNw+F64I8WurmbS/fdROEUlZx1f\n8ACES/f7vHtDi75GoxlXZNw7hi/6jlRumyJTJWvFe9NUF4cAA2PIQm5O9I2i/LlhIkGP05ddy/yC\n32rR12g0mnwTKPLF3lU+/Y3GVACKfM/KQE+KoGUSsGy1kJvx6Uej2XOYRftv6WcQgSB2yo8I0qKv\n0Wg0+SVaoxZQLUdZ+CVR9SVQVKos+niv6qjlSd+n7/e7HSz0ZmH+LH0RDOF5fk/d6MiasuwPWvQ1\nGs24omCi8qEn+iUAZqAYgKIy5b4Z6FbNVTxMDIb2wDVLlSUuIvkLrRTBINIVUD4TjNGXZC36Go1m\nXBGMBjC8NPF0GI6/AmfuJQAUVSj3TbyjGwCJgZBDa+FUf+1rANka+/lAFFUgseHinduUjA5a9DUa\nzbhCCEFAxulv78c59gukTeXMD5WWYIs4Ax09AHjSQDBU9IsWvIfZq1dhluQvtNIIhZCOx8CaRgaW\nLMnbeYdj9Cv2azQazSFGSKTYHpnNC+dfSeOEk6HsCOyScgrNjbRvD7N1dQcDZowSRr/qpQgEQEpa\nf/BDZCpFw8MPjernaUtfo9GMO8ykqpnz+pwraS87AgCruJJpoedo3AKLfrAMYBef/mggAn5ZiJaW\nvD5BDIcWfY1GM+7oDdfsMhbf2Mqs0D+GjBkHytIH0q2tmCXFo/55WvQ1Gs244/gPzNhlbPMll2N0\nDvDJc1+keooSXyHkqM8lU8hNDgxgFGvR12g0mrxz5FlTmRFr22U8nSohmt7MjGNULH/aOgBVLwO5\n6p3avaPRaDSjRKhw11LJKVEHax4j+o+rAUhbBaM+DzFY9LWlr9FoNKNDuDhXKrlyQpBAQwMprwoS\n3URQdXhSB0L0Bxdy01U2NRqNZnSIlKpkrGJnBx/+6knYk+pJtfXDgu8QNlSsfsqM7ukUeUHY2r2j\n0Wg0o06oKDjkfWBiPemt2+CEK4ie9RkAJvW9OOrzGNyR60C4d3RylkajGZcEKsuBPixfaK3ycrz+\nfrxEAqukmnOXfwpRUDbq8xi6kKtFX6PRaEaF6rmTqJrUyjs/egwAVkU5AE5bO4GCKqQUGDh7OkVe\nGOzTz/TNHU206Gs0mnGJFTD50JePz743fcF121phQiV4IGR61OeRadICYOSxeudwaJ++RqPRAFaZ\nb+m3t0NBFdITEBp9EbYqVKnnmptvGvXPAm3pazQaDTDUvUMkhiyaiKg/bNQ/1ywpYdaqldlmLaON\ntvQ1Go0GsGIqC9dpV5m6UgQQwdG39IEDJvigRV+j0WgAlRlrFBfjtvnlGRwHYZoHd1KjgBZ9jUaj\n8QlOmcLAK6/Qfs89pLdvB3vsecC16Gs0Go1P8fv/g+TadbR891ZgaGTNWGFEoi+EWCCEeFMIsU4I\n8aXdbK8XQjwthHhVCPG6EOKcQdu+7B/3phDiPfmcvEaj0eST4ve+l/D8Y7LvhWUfxNmMDnsVfSGE\nCdwBnA0cBiwUQuy8pP3fwCNSynnAR4Gf+Mce5r8/HFgA/MQ/n0aj0RxyGJEIk+6/n7LLLlMD3ug3\nUTnQjMTSPw5YJ6XcIKVMAQ8B5++0jwSK/NfFwHb/9fnAQ1LKpJRyI7DOP59Go9EckgghCEyqB8Dt\n6jzIs8k/IxH9WmDroPfb/LHBfB24SAixDfgrcM0+HKvRaDSHFFZlJQBOe8dBnkn+yddC7kLgF1LK\nOuAc4H4hxIjPLYS4TAixRAixpLW1NU9T0mg0mreGVa4StdyO8Sn6jcDEQe/r/LHBXAo8AiClfAEI\nAeUjPBYp5V1SyvlSyvkVfkqyRqPRHCzMmKrDI8epT/8VYLoQYrIQIoBamF200z5bgDMAhBCzUaLf\n6u/3USFEUAgxGZgOvJyvyWs0Gs1oYFVWUHHttdT96EcHeyp5Z69BqFJKRwhxNfA3wATulVKuEELc\nCCyRUi4CPg/cLYS4FrWo+0kppQRWCCEeAVYCDvAZKaU7Whej0Wg0+UAIQfnllx3saYwKQmnzocP8\n+fPlkiVLDvY0NBqN5m2FEOLfUsr5e9tPZ+RqNBrNOEKLvkaj0YwjtOhrNBrNOEKLvkaj0YwjtOhr\nNBrNOEKLvkaj0YwjtOhrNBrNOOKQi9MXQrQCm/fjFOVAW56mM5bQ92V49L0ZHn1vhudQuzeTpJR7\nrWNzyIn+/iKEWDKSBIXxhr4vw6PvzfDoezM8b9d7o907Go1GM47Qoq/RaDTjiLEo+ncd7Akcouj7\nMjz63gyPvjfD87a8N2POp6/RaDSa4RmLlr5Go9FohmHMiL4QYoEQ4k0hxDohxJcO9nwONEKIe4UQ\nLUKI5YPGYkKIJ4QQa/2fpf64EELc7t+r14UQRx+8mY8+QoiJQoinhRArhRArhBD/5Y+P6/sjhAgJ\nIV4WQrzm35dv+OOThRAv+df/sN88Cb8Z0sP++EtCiIaDOf8DgRDCFEK8KoT4s//+bX9vxoToCyFM\n4A7gbOAwYKEQ4rCDO6sDzi+ABTuNfQn4h5RyOvAP/z2o+zTd/3cZ8NMDNMeDhQN8Xkp5GHAC8Bn/\n92O8358kcLqUci5wFLBACHEC8B3gNinlNKAT1Q4V/2enP36bv99Y57+AVYPev/3vjZTybf8POBH4\n26D3Xwa+fLDndRDuQwOwfND7N4Ea/3UN8Kb/+k5g4e72Gw//gD8BZ+r7M+SeRIClwPGohCPLH8/+\nbaG6553ov7b8/cTBnvso3pM6lDFwOvBnQIyFezMmLH2gFtg66P02f2y8UyWlbPJfNwNFEf2AAAAC\nAklEQVRV/utxe7/8x+55wEvo+5NxXywDWoAngPVAl5TS8XcZfO3Z++Jv7wbKDuyMDyg/AL4IZLqj\nlzEG7s1YEX3NXpDKBBnXoVpCiALgd8DnpJQ9g7eN1/sjpXSllEehrNrjgFkHeUqHBEKI9wIt8v+3\nb/cqDQRRGIbfA/5WBsFCUJCArZWFoIV1CisLQdDCqxDBSxC8AEtRECyCpcZeEUUFwR+wEVEQtLY4\nFnMiwcoiZmXneyBkd2aLma84kDMb97Oi19JuZSn6T8Boy/1IjOXuxcyGAeL7Ncazy8vMukkFf9vd\n92NY+QR3fweOSS2Lipl1xVTr3r9zifkB4K3DS+2UaWDOzB6BXVKLZ5MSZFOWon8KjMfJeg+wANQL\nXtN/UAeW43qZ1Mtuji/FWypTwEdLm6N0zMyALeDG3TdaprLOx8yGzKwS1/2kc44bUvGfj8d+5tLM\nax5oxC+k0nH3VXcfcfcxUj1puPsiZcim6EOFNh661IBbUk9yrej1FLD/HeAZ+CT1GldIPcUj4A44\nBAbjWSO97fQAXAGTRa//j7OZIbVuLoGL+NRyzweYAM4jl2tgPcarwAlwD+wBvTHeF/f3MV8teg8d\nymkWOChLNvpHrohIRsrS3hERkV9Q0RcRyYiKvohIRlT0RUQyoqIvIpIRFX0RkYyo6IuIZERFX0Qk\nI19tMYgkpcC6vwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106be3cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out of sample results\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
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